Instructions to use Rustem/roberta-base-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rustem/roberta-base-trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Rustem/roberta-base-trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Rustem/roberta-base-trained") model = AutoModelForMaskedLM.from_pretrained("Rustem/roberta-base-trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Rustem/roberta-base-trained: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Rustem/roberta-base-trained/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Rustem/roberta-base-trained@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Rustem/roberta-base-trained/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- 32b25f7db6f2773c07dabe20b20ecb4fc339c7f84f0a2f9f168b366b4b70e44d
- Size of remote file:
- 499 MB
- SHA256:
- 5db4be63e0824000655a069974c1f4bd6e2137e0b6c32d960cee86d37d75b8df
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